Arboreal Identification Supported by Fuzzy Modeling for Trunk Texture Recognition

Adriano Bressane, Felipe Hashimoto Fengler, Sandra Regina Monteiro Masalskiene Roveda, José Arnaldo Frutuoso Roveda, Antonio Cesar Germano Martins

Abstract


Due to the natural variability of the arboreal bark there are texture patterns in trunk images with values belonging to more than one species. Thus, the present study analyzed the usage of fuzzy modeling as an alternative to handle the uncertainty in the trunk texture recognition, in comparison with other machine learning algorithms. A total of 2160 samples, belonging to 20 tree species from the Brazilian native deciduous forest, were used in the experimental analyzes. After transforming the images from RGB to HSV, 70 texture patterns have been extracted based on first and second order statistics. Secondly, an exploratory factor analysis was performed for dealing with redundant information and optimizing the computational effort. Then, only the first dimensions with higher cumulative variability were selected as input variables in the predictive modeling. As a result, fuzzy modeling reached a generalization ability that outperformed algorithms widely used in classification tasks, besides of obtaining an almost perfect agreement with the classifier with the best accuracy in the validation tests. Therefore, the fuzzy modeling can be considered as a competitive approach, with reliable performance in arboreal trunk texture recognition.

Keywords


soft computing; image processing; pattern matching; bioinformatics

Full Text:

PDF

References


A. Porebski, N. Vandenbroucke, and L. Macaire, “Iterative feature selection for color texture classification,” in Image Processing, 2007. ICIP 2007. IEEE International Conference on, vol. 3, pp. III–509, IEEE, 2007.

Y.-Y. Wan, J.-X. Du, D.-S. Huang, Z. Chi, Y.-M. Cheung, X.-F. Wang, and G.-J. Zhang, “Bark texture feature extraction based on statistical texture analysis,” in Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on, pp. 482–485, IEEE, 2004.

Z.-K. Huang, C.-H. Zheng, J.-X. Du, and Y.-y. Wan, “Bark classification based on textural features using artificial neural networks,” in International Symposium on Neural Networks, pp. 355–360, Springer, 2006.

J. Boman, “Tree species classification using terrestrial photogrammetry.

S. Fiel and R. Sablatnig, “Automated identification of tree species from images of the bark, leaves and needles,” in 16th Computer Vision Winter Workshop. Citeseer, p. 67, Citeseer, 2011.

Z.-K. Huang, “Bark classification using rbpnn based on both color and texture feature,” International Journal of Computer Science and Network Security, vol. 6, no. 10, pp. 100–103, 2006.

A. Bressane, J. A. F. Roveda, and A. C. Martins, “Statistical analysis of texture in trunk images for biometric identification of tree species,” Environmental monitoring and assessment, vol. 187, no. 4, p. 212, 2015.

L. A. Zadeh, “Fuzzy sets,” Information and control, vol. 8, no. 3, pp. 338–353, 1965.

L. A. Zadeh, “Is there a need for fuzzy logic?,” Information sciences, vol. 178, no. 13, pp. 2751–2779, 2008.

W. Pedrycz and F. Gomide, Fuzzy systems engineering: toward human-centric computing. John Wiley & Sons, 2007.

A. Bressane, P. S. Mochizuki, R. M. Caram, and J. A. F. Roveda, “A system for evaluating the impact of noise pollution on the population’s health,” Reports in public health, vol. 32, no. 5, 2016.

D. Liu and Z. Zou, “Water quality evaluation based on improved fuzzy matterelement method,” Journal of Environmental Sciences, vol. 24, no. 7, pp. 1210–1216, 2012.

L. Liu, J. Zhou, X. An, Y. Zhang, and L. Yang, “Using fuzzy theory and information entropy for water quality assessment in three gorges region, china,” Expert Systems with Applications, vol. 37, no. 3, pp. 2517–2521, 2010.

A. Lermontov, L. Yokoyama, M. Lermontov, and M. A. S. Machado, “River quality analysis using fuzzy water quality index: Ribeira do iguape river watershed, brazil,” Ecological Indicators, vol. 9, no. 6, pp. 1188–1197, 2009.

J. Ascough, H. Maier, J. Ravalico, and M. Strudley, “Future research challenges for incorporation of uncertainty in environmental and ecological decisionmaking,” Ecological modelling, vol. 219, no. 3, pp. 383–399, 2008.

V. Adriaenssens, B. De Baets, P. L. Goethals, and N. De Pauw, “Fuzzy rule based models for decision support in ecosystem management,” Science of the Total Environment, vol. 319, no. 1, pp. 1–12, 2004.

W. Silvert, “Fuzzy indices of environmental conditions,” Ecological Modelling, vol. 130, no. 1, pp. 111–119, 2000.

H. Ishibuchi and T. Nakashima, “Effect of rule weights in fuzzy rule-based classification systems,” IEEE Transactions on Fuzzy Systems, vol. 9, no. 4, pp. 506–515, 2001.

H. Singh, M. M. Gupta, T. Meitzler, Z.-G. Hou, K. K. Garg, A. M. Solo, and L. A. Zadeh, “Real-life applications of fuzzy logic.,” Adv. Fuzzy Systems, vol. 2013, pp. 581879–1, 2013.

C. Arunpriya and A. S. Thanamani, “Fuzzy inference system algorithm of plant classification for tea leaf recognition,” Indian Journal of Science and Technology, vol. 8, no. S7, pp. 179–184, 2015.

L. S. Riza, C. N. Bergmeir, F. Herrera, and J. M. Benítez Sánchez, “frbs: Fuzzy rule-based systems for classification and regression in r,” Journal of Statistical Software, vol. 65, no. 6, pp. 1–30, 2015.

R. C. Gonzales and R. E. Woods, Digital image processing, vol. 3. Pearson Prentice Hall, 2008.

R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on systems, man, and cybernetics, vol. 3, no. 6, pp. 610–621, 1973.

W. R. E. Gonzales, R. C. and S. L. Eddins, Digital image processing using MATLAB, vol. 2. Gatesmark Publishing, 2009.

A. B. Costello and J.W. Osborne, “Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis,” Pan-Pacific Management Review, vol. 12, no. 2, pp. 131–146, 2009.

A. G. Yong and S. Pearce, “A beginnerÂs guide to factor analysis: Focusing on exploratory factor analysis,” Tutorials in Quantitative Methods for Psychology, vol. 9, no. 2, pp. 79–94, 2013.

Z. Chi, H. Yan, and T. Pham, Fuzzy algorithms: with applications to image processing and pattern recognition, vol. 10. World Scientific, 1996.

D. Nauck and R. Kruse, “A neuro-fuzzy method to learn fuzzy classification rules from data,” Fuzzy sets and Systems, vol. 89, no. 3, pp. 277–288, 1997.

S. Abe and R. Thawonmas, “A fuzzy classifier with ellipsoidal regions,” IEEE Transactions on fuzzy systems, vol. 5, no. 3, pp. 358–368, 1997.

A. Gonzblez and R. Pérez, “Slave: A genetic learning system based on an iterative approach,” IEEE Transactions on Fuzzy Systems, vol. 7, no. 2, pp. 176–191, 1999.

J. Carletta, “Assessing agreement on classification tasks: the kappa statistic,” Computational linguistics, vol. 22, no. 2, pp. 249–254, 1996.

E. Bauer and R. Kohavi, “An empirical comparison of voting classification algorithms: Bagging, boosting, and variants,” Machine learning, vol. 36, no. 1-2, pp. 105–139, 1999.




DOI: https://doi.org/10.5540/tema.2018.019.01.111

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.


Trends in Computational and Applied Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)

 

Indexed in:

                       

         

 

Desenvolvido por:

Logomarca da Lepidus Tecnologia